Abstract
Long-term travel time prediction (TTP) can be an important planning tool for both freight transport and public transport companies. In both cases it is expected that the use of long-term TTP can improve the quality of the planned services by reducing the error between the actual and the planned travel times. However, for reasons that we try to stretch out along this paper, long-term TTP is almost not mentioned in the scientific literature. In this paper we discuss the relevance of this study and compare three non-parametric state-of-the-art regression methods: Projection Pursuit Regression (PPR), Support Vector Machine (SVM) and Random Forests (RF). For each one of these methods we study the best combination of input parameters. We also study the impact of different methods for the pre-processing tasks (feature selection, example selection and domain values definition) in the accuracy of those algorithms. We use bus travel time's data from a bus dispatch system. From an off-the-shelf point-of-view, our experiments show that RF is the most promising approach from the three we have tested. However, it is possible to obtain more accurate results using PPR but with extra pre-processing work, namely on example selection and domain values definition.

Abstract
The goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.

Abstract
In this paper we study the deviation of bus trip duration and its causes. Deviations are obtained by comparing scheduled times against actual trip duration and are either delays or early arrivals. We use distribution rules, a kind of association rules that may have continuous distributions on the consequent. Distribution rules allow the systematic identification of particular conditions, which we call contexts, under which the distribution of trip time deviations differs significantly from the overall deviation distribution. After identifying specific causes of delay the bus company operational managers can make adjustments to the timetables increasing punctuality without disrupting the service. © Springer-Verlag Berlin Heidelberg 2012.

Abstract
Industrial companies are faced today with strong competition and fast changing markets. To reduce the risk and evaluate alternatives, simulation is considered to be a good tool. Industrial company's simulation must have sufficient detail to be useful but, at the same time, take into account the external environmental behaviour. Distributed simulation has specific characteristics to study these problems but its complexity limits its use in industry as stated in the literature. To overcome these difficulties, a framework is proposed, offering communication and synchronization mechanisms, which allow the integration of simulation models in the industrial area. To promote semantic interoperability, a business language is also proposed based on the OASIS Universal Business Language (UBL). As industrial companies have a high level of complexity and autonomy, the simulation models work as agents using Agent-based Modelling and Simulation (ABMS) techniques to achieve a more realistic behaviour of the whole system.

Abstract
A method for the separation and fractionation of the major whey proteins from a whey protein concentrate (WPC80) by anion-exchange chromatography coupled to a Fast Protein Liquid Chromatography (FPLC) system is proposed. The method is based on the use of an ionic column (Mono Q) and a salt gradient elution by increasing the ionic strength of the elution buffer (Tris-HCl 20 mM plus 0 to 1 M NaCl). The proposed method was found to be suitable to fractionate the major whey proteins from the WPC80 in different fractions, namely one fraction containing all the alpha-Lactalbumin and immunoglobulins; another fraction containing all the bovine serum albumin; and two distinct fractions each containing a different variant of p-Lactoglobulin. A 60.5% (w/w) recovery of the two main p-Lactoglobulin variants was obtained.

Abstract
Purpose - The purpose of this paper is to study the alignment measurement in collaborative networks, using the fit concept and predictive performance measurement as its main enablers. A performance prediction approach is used in order to control a collaborative business network based not only in present and past performance measurements of each partner, but also taking into account the future behaviour of the intra- and inter-organisational processes performance. Design/methodology/approach - An exploratory case study was applied to a Brazilian collaborative network and mathematical approaches normally used in control theory were adopted to support alignment measurement. Findings - The use of predictive measurements to manage the alignment between the results of inter-organisational processes and performance targets set by the collaborative network. Research limitations/implications - This approach was applied in a specific supply chain network, based on three industrial companies. For other network typologies it will be necessary to evaluate the alignment that can be achieved. Practical implications - This predictive approach makes it possible to manage performance pro-actively using feedforward and feedback control. Therefore, tools that consider performance estimation are used based on a data fusion approach, with a proper combination of leading and lagging measurements, which make it possible to use forecasting methods and tools to achieve good predictions. Originality/value - The paper introduces an approach to alignment measurement leveraged by the new paradigm of performance prediction and presents an alignment metric for collaborative networks. © Emerald Group Publishing Limited.